Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

2017 ◽  
Vol 25 (1) ◽  
pp. 116-122 ◽  
Author(s):  
Congli Mei ◽  
Yong Su ◽  
Guohai Liu ◽  
Yuhan Ding ◽  
Zhiling Liao
Keyword(s):  
Gaussian Mixture ◽  
Soft Sensor ◽  
Fermentation Processes ◽  
Mixture Regression ◽  
Sensor Development

Soft Sensor for Multiphase and Multimode Processes Based on Gaussian Mixture Regression

2014 ◽  
Vol 47 (3) ◽  
pp. 1067-1072
Author(s):  
Xiaofeng Yuan ◽  
Zhiqiang Ge ◽  
Hongwei Zhang ◽  
Zhihuan Song ◽  
Peiliang Wang
Keyword(s):  
Gaussian Mixture ◽  
Soft Sensor ◽  
Mixture Regression

scholarly journals A KPI-Based Probabilistic Soft Sensor Development Approach that Maximizes the Coefficient of Determination

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3058 ◽  
Author(s):  
Yue Zhang ◽  
Xu Yang ◽  
Yuri Shardt ◽  
Jiarui Cui ◽  
Chaonan Tong
Keyword(s):  
Em Algorithm ◽  
Probability Model ◽  
Joint Probability ◽  
Gaussian Mixture ◽  
Training Sample ◽  
Soft Sensor ◽  
Advanced Technology ◽  
Coefficient Of Determination ◽  
Joint Probability Distribution ◽  
Sensor Development

Advanced technology for process monitoring and fault diagnosis is widely used in complex industrial processes. An important issue that needs to be considered is the ability to monitor key performance indicators (KPIs), which often cannot be measured sufficiently quickly or accurately. This paper proposes a data-driven approach based on maximizing the coefficient of determination for probabilistic soft sensor development when data are missing. Firstly, the problem of missing data in the training sample set is solved using the expectation maximization (EM) algorithm. Then, by maximizing the coefficient of determination, a probability model between secondary variables and the KPIs is developed. Finally, a Gaussian mixture model (GMM) is used to estimate the joint probability distribution in the probabilistic soft sensor model, whose parameters are estimated using the EM algorithm. An experimental case study on the alumina concentration in the aluminum electrolysis industry is investigated to demonstrate the advantages and the performance of the proposed approach.

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